Originally published at Talent Analytics, Inc.
1. Identify a business problem to solve.
Almost everyone working on modern predictive analytics discusses the need for a defined business problem before engaging in a predictive project. And yet, the #1 question I get in speaking with businesses is “I need to do a predictive project, but I don’t know what to work on”.
Without a specific problem to solve, your analyst or vendor will do nothing more than crunch data hoping to find something interesting. Crunching data without a specific objective, is a very expensive and typically a very unproductive use of your company’s time and money.
Consider these examples:
You need to first know what you’re looking for before you embark on a predictive project.
2. Decide if you want to solve an <<employee-related process problem in HR>> — or an <<employee related business problem in a line of business>>.
3. Combine HR data with line of business data.
If you are looking to predict and solve a workforce problem in the line of business (i.e. increase sales, reduce errors, increase calls per day and the like…) the outcome data in the line of business exists in software systems in the lines of business, not in HR.
As an example, sales performance, or calls per day data exists in Sales Operations or the Call Center or some other non-HR database somewhere.
You can’t predict which sales candidates are going to make their sales numbers without sales data from the sales department. You need to use line of business data as well as HR data. Unless you only want to predict something that impacts HR, you’ll need data from the line of business as well.
4. Go beyond predicting trends. “Individual” predictions deliver the greatest ROI.
Many departments have been forecasting trends for years – and in fact many predictive projects we hear about are in fact older-school forecasting projects. We need to move beyond forecasting to deliver the kinds of ROI that excites your C-Suite.
Forecasting examples include:
While forecasting is extremely necessary – it is quite different than modern predictive analytics initiatives. To reap the ROI of modern predictive capabilities – organizations need to move to predicting to the individual.
Predicting “to an individual” examples include:
The ability to predict to this level of granularity should be the goal of modern predictive projects. ROI is higher because it helps your company to take specific action with high cost or high revenue potential targets.
5. Go beyond predicting flight risk of existing employees. Make a prediction about flight risk before you hire a candidate.
Many companies focus on predicting the flight risk of existing employees as an early predictive project. This reminds me of a bank predicting which loans will fail “after” they’ve already loaned money. After the relationship is extended is the wrong time. It’s too late.
Modern predictive analytics allows you to predict “before”. That’s the point. Predict before you make the mistake. Banks put a lot of effort into creating predictive models that predict your probability of paying or defaulting on a loan before they extend the loan.
About the Author
Greta Roberts is an acknowledged influencer in the field of predictive workforce analytics. Since co-founding Talent Analytics in 2001, she has established Talent Analytics, Corp. as the globally recognized leader in predicting an individual’s business performance, pre-hire and post-hire.
She has led the firm to use predictive analytics to solve line of business challenges making Talent Analytics one of the only firms in the world predicting business outcomes. Examples include predicting someone’s probability of making their sales quota, or being able to process a certain number of calls, or make errors, and the like.
Greta leads the company in developing predictive solutions that can be easily deployed into employee operations, to teams without a background in analytics, statistics or math. This strategy has led to the development of Talent Analytics’ award winning predictive cloud platform Advisor.
In addition to being a contributing author to numerous predictive analytics books, she is regularly invited to comment in the media and speak at high end predictive analytics and business events around the world. Through recognition of her commitment and leadership, Greta was elected and continues to be Chair of Predictive Analytics World for Workforce, an innovative, annual predictive analytics event dedicated to solving workforce challenges. She is an Instructor on Predictive Analytics for HR and Workforce at UC Irvine; she is a faculty member with the International Institute for Analytics (IIA), is a member of the INFORMS Analytics Certification Board.
Follow Greta on twitter @gretaroberts